1. Field of the Invention
The present invention relates to a method for cancelling ghost effects in an image signal processor, and in particular, to an improved method for cancelling ghost effects in an image signal processor which is capable of enhancing a performance of cancelling ghost effects in a system using a finite impulse response (hereinafter, called FIR) filter and an infinite impulse response (hereinafter, called IIR) filter.
2. Description of the Prior Art
Recently, multilateral programs for cancelling ghost effects occurring in an image signal processor like a TV set have been studied. A general method for cancelling ghost effects is that a transmitter transmits a ghost cancelling reference (hereinafter, called GCR) signal, and a receiver compares the GCR signal with the stored GCR signal. To do this, each GCR signal differently set in each country is transmitted.
FIG. 1 is a block diagram of a digital filter of a ghost effect canceler according to the conventional art, which includes an FIR filter 10 for filtering an input digital signal (Xn) in accordance with a coefficient of a filter and cancelling ghost effects, an IIR filter 9 for filtering an output from the FIR filter 10 in accordance with a coefficient of a filter and cancelling ghost effects, and an adder 12 for adding the outputs from the FIR filter 10 and the FIR filter to define the IIR filter 9.
The operation of the digital filter in a conventional ghost effect canceler having the above construction will now be described in detail.
First, when the digital signal is inputted from an analog/digital (A/D) converter (not illustrated), the FIR filter 10 cancels a ghost effect in the digital signal (Xn) by repeating filterings by predetermined times in accordance with a coefficient of a filter, and the IIR filter 9 receives an output from the FIR filter 10 and again cancels ghost effects in the digital signal by repeating filterings by predetermined times. And the output from the IIR filter 11 is converted into an analog signal in a digital/analog (D/A) converter (not illustrated).
Here, to obtain the coefficients from the FIR filter 10 and the IIR filter 11, a precise algorithm is required, and the most widely adopted one is a least mean square (hereinafter, called LMS) algorithm as shown in FIG. 2.
The LMS algorithm will now be explained.
It is assumed that if an input signal X(n) passes through an unacknowledge system 13, it becomes a GCR signal d(n) which serves as a reference signal.
Here, a subtractor 15 subtracts an output y(n) from an adaptive FIR filter 14 from the GCR signal d(n) to obtain a resultant value e(n) and feeds back the resultant value e(n) to the adaptive FIR filter 14. Here, until the output value e(n) from the subtractor 15 becomes zero (0), the adaptive FIR filter 14 performs a filtering repeatedly to obtain the best coefficient of the filter. The above procedure is the LMS algorithm.
However, since the LMS algorithm is generally used only when the coefficient of the FIR filter is updated, it cannot be adopted in the system using both the FIR filter and the IIR filter.
Further, since the digital filter of the conventional ghost effect canceler sequentially updates the FIR coefficient and the IIR coefficient, when an error occurs in updating the FIR coefficient, the update of the IIR coefficient is influenced, and as a result, a ghost effect quality is not properly cancelled from the image signal.
Moreover, since the IIR filter has a feedback construction, when the FIR coefficient is computed, long ghost effects which must be processed in a region of the IIR filter influence the coefficient, resulting in lowering a performance of cancelling the ghost effects in the system.